Thomas Mortier
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View article: Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation
Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation Open
Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the glob…
View article: Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics
Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics Open
Automatic cell-type annotation methods assign cell-type labels to new, unlabeled datasets by leveraging relationships from a reference RNA-seq atlas. However, new datasets may include labels absent from the reference dataset or exhibit fea…
View article: Valid Prediction Intervals for Weather Forecasting with Conformal Prediction
Valid Prediction Intervals for Weather Forecasting with Conformal Prediction Open
In recent years, machine learning has emerged as a promising alternative to numerical weather prediction models, offering the potential for cost-effective and accurate forecasts. However, a significant limitation of current machine learnin…
View article: Benchmarking Deep Learning Models for Probabilistic Subseasonal Forecasting of Heat Extremes
Benchmarking Deep Learning Models for Probabilistic Subseasonal Forecasting of Heat Extremes Open
Predicting climate extremes such as droughts, heatwaves, and heat stress episodes remains a critical challenge in Earth system sciences. Current state-of-the-art methods often fail to deliver reliable forecasts, especially at subseasonal-t…
View article: A calibration test for evaluating set-based epistemic uncertainty representations
A calibration test for evaluating set-based epistemic uncertainty representations Open
The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilistic predictors, also known as credal sets. One popular way o…
View article: Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation
Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation Open
Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the glob…
View article: Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity Open
Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conformal prediction framework to hierarchical classification, whe…
View article: Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics
Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics Open
Automatic cell type annotation methods assign cell type labels to new, unlabeled datasets by leveraging relationships from a reference RNA-seq atlas. However, new datasets may include labels absent from the reference dataset or exhibit fea…
View article: A framework for tracing timber following the Ukraine invasion
A framework for tracing timber following the Ukraine invasion Open
Scientific testing including stable isotope ratio analysis (SIRA) and trace element analysis (TEA) is critical for establishing plant origin, tackling deforestation and enforcing economic sanctions. Yet methods combining SIRA and TEA into …
View article: Uncertainty-aware single-cell annotation with a hierarchical reject option
Uncertainty-aware single-cell annotation with a hierarchical reject option Open
Motivation Automatic cell type annotation methods assign cell type labels to new datasets by extracting relationships from a reference RNA-seq dataset. However, due to the limited resolution of gene expression features, there is always unc…
View article: Uncertainty-aware single-cell annotation with a hierarchical reject option
Uncertainty-aware single-cell annotation with a hierarchical reject option Open
Automatic cell type annotation methods assign cell type labels to new datasets by extracting relationships from a reference RNA-seq dataset. However, due to the limited resolution of gene expression features, there is always uncertainty pr…
View article: On the Calibration of Probabilistic Classifier Sets
On the Calibration of Probabilistic Classifier Sets Open
Multi-class classification methods that produce sets of probabilistic classifiers, such as ensemble learning methods, are able to model aleatoric and epistemic uncertainty. Aleatoric uncertainty is then typically quantified via the Bayes e…
View article: Set-valued prediction in hierarchical classification with constrained representation complexity
Set-valued prediction in hierarchical classification with constrained representation complexity Open
Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of classes instead of a single class. In this paper, we focus on hi…